As the Internet continues to increase in popularity, the amount of available content continues to increase geometrically. In particular, the number of moving image-type UCCs (User Created Content) is increasing explosively. In this environment, users with limited information and limited time are having difficulty finding content that satisfies their interests. Recommendation systems are thus being used more and more to recommend appropriate content to users based on their inclinations and preferences. Known recommendation systems recommend content by determining a user neighborhood having similar inclinations as a target user and utilizing a relationship between the target user and a user of the user neighborhood.
Conventional recommendation technology, however, has the following limitations. First, in the case of explicit data collection, the actual number of contents that are purchased, used, and/or evaluated by users is often small and, thus, the amount of content that can be recommended is limited.
Also, conventional recommendation technologies often select users similar to a target user based on those users who used the same content as the target user. Thus, the range of similar users may be limited and, consequently, the range of recommendable content also may be limited, possibly resulting in the recommendation of undesired content.
Moreover, in cases where the number of contents is larger than the number of users, the number of users who used the same content as the target user is usually small. Thus, finding users similar to a target user is difficult.
Another limitation of conventional recommendation technologies is that similar users are selected on the basis of content used by a target user in the past. Then, only a content related to the subject in which the target user had an interest in the past is recommended.
And because similar users are selected from those who used the same content as a target user, coverage for content beyond that actually used by the similar and target users is very low, for example, only about 10% to 30%.